Method for detecting anomalies on or in a surface of a structure

ABSTRACT

Described herein is a method of detecting anomalies on a surface of a structure. The method may comprise taking a thermal image of the surface of the structure. The method may further comprise taking a visual image of the surface of the structure. The method may then comprise conducting a thermal image numerical analysis on the thermal image. The thermal image numerical analysis may comprise obtaining a thermal image numerical value table. The thermal image numerical analysis may then comprise obtaining a surface nominal thermal value of the surface material. The thermal image numerical analysis may then comprise eliminating a first subset of pixels having a thermal value within a nominal thermal variation from the plurality of pixels. The thermal image numerical analysis may then comprise comparing the thermal value of each pixel of the plurality of pixels not in the first subset of pixels to the surface nominal thermal value to identify at least one anomaly. The thermal image numerical analysis method may then comprise removing a first number (n1) of first anomalies from the thermal image numerical analysis. Finally, the method may comprise comparing the first anomalies from the thermal image numerical analysis to the visual image.

CROSS REFERENCES AND PRIORITIES

This Application claims priority from International Application No.PCT/US2018/057343 filed on 22 Oct. 2019 and U.S. Provisional ApplicationNo. 62/751,441 filed on 26 Oct. 2018 the teachings of each of which areincorporated by reference herein in their entirety.

BACKGROUND

Many attempts have been made to detect and monitor the presence ofcertain anomalies, such as moisture or energy loss, on specific surfacessuch as the roof of a building. One such method is disclosed in U.S.Pat. No. 6,104,298 which discloses a roof moisture detection assemblyincludes an imaging system for obtaining thermal and visible images of aroof surface, an imaging system support structure for mounting theimaging system in a position elevated relative to the roof surface, areference target mounted on the roof surface, and an image-processingsystem adapted to compare current thermal and visible images of the roofsurface with previous thermal and visible images of the roof surface anddetect shapes and areas of anomalous features, and to compare thecurrent thermal and visible images with each other and detect shapes andareas of anomalous features.

The known methods for detecting and monitoring anomalies on a surface,such as a roof, often suffer from an inability to distinguish anomaliesof interest from anomalies which are not of interest. For instance,known methods may be incapable of distinguishing a moisture anomalywhich is of interest and may call for repair or replacement of thesurface, from an anomaly caused by a desirable structure on the surfacesuch as a vent which is not of interest.

The need exists, therefore, for an improved method of detecting andmonitoring the presence of anomalies on a surface which can eliminateanomalies which are not of interest.

SUMMARY

A method of detecting anomalies on a surface of a structure isdisclosed. The method may comprise the steps of taking a thermal imageof the surface of the structure, and conducting a thermal imagenumerical analysis on the thermal image.

The thermal image numerical analysis may comprise the steps of obtaininga thermal image numerical value table comprising a plurality of pixels,obtaining a surface nominal thermal value, eliminating a first subset ofpixels from the plurality of pixels, comparing a thermal value of eachpixel of the plurality of pixels not in the first subset of pixels tothe surface nominal thermal value to identify at least one anomaly, andremoving a first number (n₁) of first anomalies from the thermal imagenumerical value table. Each pixel of the plurality of pixels may have anX coordinate, a Y coordinate, and a Z coordinate with the X coordinateand the Y coordinate corresponding to a specific spatial location of thepixel on the surface of the structure, and the Z coordinatecorresponding to a thermal value of said pixel. The thermal value may bea real number greater than or equal to 0. The first subset of pixels mayhave a thermal value which is within a nominal thermal variation of asurface material of the surface to the surface nominal thermal value. n₁may be an integer greater than or equal to 0. The first anomalies may beselected from the group consisting of at least one non-delta anomaly, atleast one non-signature anomaly, at least one non-pattern anomaly, atleast one non-visual anomaly, and at least one non-cavity anomaly.

In some embodiments, the method may further comprise comparing thethermal value of a second number (n₂) of second anomalies from thethermal image numerical value table wherein n₂ is an integer greaterthan or equal to 0 to the surface nominal thermal value in order toidentify a type of anomaly selected from the group consisting of anenergy loss anomaly, and a live load anomaly.

In some embodiments, the thermal image may be taken at least 60 minutesafter sunset on a sunny day. In some embodiments, the thermal image maybe taken at a time selected from the group consisting of no more thanabout 6 hours after sunset, no more than about 5 hours after sunset, nomore than about 4 hours after sunset, no more than about 3 hours aftersunset, and no more than about 2 hours after sunset.

In some embodiments, the thermal image may be taken on a day in which ageographic location of the surface experiences no more than about 40%cloud cover during at least the 4 hours preceding sunset. In otherembodiments, the thermal image may be taken on a day in which ageographic location of the surface experiences no more than about 30%cloud cover during at least the 4 hours preceding sunset. In still otherembodiments, the thermal image may be taken on a day in which ageographic location of the surface experiences no more than about 20%cloud cover during at least the 4 hours preceding sunset. In still otherembodiments, the thermal image may be taken on a day in which ageographic location of the surface experiences no more than about 10%cloud cover during at least the 4 hours preceding sunset.

In some embodiments the method may further comprise the steps of: takinga visual image of the surface of the structure; and comparing the firstanomalies from the thermal image numerical analysis to the visual image.The visual image may be taken at or around solar noon on the same day asthe thermal image.

In some embodiments, obtaining the surface nominal thermal value maycomprise calculating the most prevalent thermal value across a secondsubset of the pixels. In some embodiments, the second subset of theplurality of pixels may comprise a percentage of total pixels of thethermal image selected from the group consisting of at least 20% of thetotal pixels, at least 35% of the total pixels, at least 50% of thetotal pixels, at least 65% of the total pixels, at least 90% of thetotal pixels, at least 99% of the total pixels, and 100% of the totalpixels.

In some embodiments, the step of comparing a thermal value of each pixelof the plurality of pixels to the surface nominal thermal value mayyield an intensity value for each thermal value corresponding to eachindividual pixel calculated according to a formula:

(MTV)−(NTV)=IV

where MTV is the thermal value of each pixel and NTV is the surfacenominal thermal value. In some embodiments, the intensity value may becalculated for each pixel of the plurality of pixels which make up thethermal image numerical value table. In other embodiments, the intensityvalue may be calculated for a third subset of the plurality of pixelscomprising a percentage of the total pixels of the thermal imageselected from the group consisting of at least 20% of the total pixels,at least 35% of the total pixels, at least 50% of the total pixels, atleast 65% of the total pixels, at least 90% of the total pixels, atleast 99% of the total pixels, and 100% of the total pixels.

In some embodiments, the intensity value of each pixel of the pluralityof pixels may be plotted on a three dimensional graph.

A method of detecting lighting anomalies on a surface of a structure isalso disclosed. The method may comprise the steps of: taking a visualimage of the surface of the structure; performing a black and whitecolor adjustment on the visual image; calculating a light intensity of aplurality of pixels within the visual image; identifying a first subsetof the plurality of pixels; identifying a second subset of the pluralityof pixels; performing a black color adjustment on the second subset ofthe plurality of pixels; identifying a third subset of the plurality ofpixels; and performing a gradation color adjustment on the third subsetof the plurality of pixels.

The visual image of the surface of the structure may be taken in color.Each pixel within the first subset of the plurality of pixels maycorrespond to a threshold light intensity. In some embodiments, thelight intensity of each pixel within the second subset of the pluralityof pixels may be below the threshold light intensity. In certainembodiments, the light intensity of each pixel within the third subsetof the plurality of pixels may be above the threshold light intensity.

In some embodiments, the visual image may be taken at a time which is nogreater than 60 minutes after sunset and no less than 60 minutes beforesunrise. The visual image may be taken by an optical camera mounted toan aircraft flying over the surface. The aircraft may be traveling at aspeed in the range of between 0 mph and 150 mph. The optical camera mayhave a shutter speed in the range of between 1/400 seconds and 1/10,000seconds.

BRIEF DESCRIPTION OF FIGURES

FIG. 1 is one embodiment of a thermal image as described herein.

FIG. 2 is one embodiment of a thermal image numerical value table asdescribed herein.

FIG. 3 is one embodiment of a three dimensional graph of intensityvalues as described herein.

DETAILED DESCRIPTION

Disclosed herein is a method of detecting anomalies on the surface of astructure. The method may include the following steps. The first stepmay comprise taking a thermal image of the surface of the structure. Thenext step may comprise taking a visual image of the surface of thestructure. The next step may comprise conducting a thermal imagenumerical analysis on the thermal image to obtain a thermal imagenumerical value table. The next step may comprise obtaining a surfacenominal thermal value (also known as a Nominal Temperature Value (NTV)).The next step may comprise comparing one or more of the thermal values(also known as Measured Temperature Value (MTV)) of the thermal imagenumerical value table to the surface nominal thermal value to identifyat least one anomaly. The next step may comprise removing a first number(n₁) of first anomalies from the thermal image numerical analysis, thefirst anomalies each being a type of anomaly which is not of interest.The next step may comprise comparing the thermal value of a secondnumber (n₂) of second anomalies from the thermal image numericalanalysis, the second anomalies each being a type of anomaly which is ofinterest.

The step of taking a thermal image of the surface of the structure maycomprise flying over the surface using an airplane, helicopter, drone,or similar aerial vehicle. The aerial vehicle may include an infraredcamera facing downwardly from the aerial vehicle towards the ground.Preferably the infrared camera is oriented towards the ground in a nadirposition. One example of an infrared camera is an SC-8313 cameraavailable from FLIR® Systems, Inc. of Wilsonville, Oreg., U.S.A.Preferably the infrared camera has a resolution of at least 320×240 withat least 640×480 being more preferred and at least 1024×1024 being mostpreferred. One example of a thermal image—which is a thermal image of aroof surface—is shown in FIG. 1 .

Preferably, the thermal image is taken at least 60 minutes after sunseton a sunny day. More preferably the thermal image is taken at a timeselected from the group consisting of no more than about 6 hours aftersunset, no more than about 5 hours after sunset, no more than about 4hours after sunset, no more than about 3 hours after sunset, and no morethan about 2 hours after sunset. By sunny day it is meant that thegeographic location where the surface is located experienced no morethan about 40% cloud cover during at least the 4 hour preceding sunsetwith no more than about 30% cloud cover during at least the 4 hourspreceding sunset being more preferred, no more than about 20% cloudcover during at least the 4 hours preceding sunset being still morepreferred, and no more than about 10% cloud cover during at least the 4hours preceding sunset being most preferred. Taking the thermal image ator after the time of sunset on a sunny day better ensures that thesurface has been exposed to thermal radiation during daytime hours whichcan be detected in the thermal image while also reducing or eliminatinginterference in the thermal image caused by thermal radiation from thesun.

Corresponding to a sunny day—it is preferred that the thermal image betaken on a day having specific environmental conditions related toprecipitation and wind speeds. Regarding precipitation, it is preferredthat the thermal image be taken at a time in which there has been noprecipitation during the preceding 12 hours with no precipitation duringthe preceding 18 hours being more preferred, no precipitation during thepreceding 24 hours being still more preferred, no precipitation duringthe preceding 36 hours being even more preferred, and no precipitationduring the preceding 48 hours being most preferred. Regarding windspeed, it is preferred that the thermal image be taken at a time inwhich the sustained wind speed is below 20 mph with sustained wind speedbelow 15 mph being more preferred, sustained wind speed below 10 mphbeing still more preferred, and sustained wind speed below 5 mph beingmost preferred.

Preferably, the thermal image is taken before a dew point convergencetime which is the time at which the ambient temperature exterior to thesurface is equal to the dew point exterior to the surface. Morepreferably, the thermal image is taken at a time when the ambienttemperature is at least about 0.5° F. above the dew point. Even morepreferably, the thermal image is taken at a time when the ambienttemperature is at least about 1.0° F. above the dew point. Mostpreferably, the thermal image is taken at a time when the ambienttemperature is at least about 2.0° F. above the dew point. Accordingly,the time for taking the thermal image may be represented as a thermalimage time window beginning at a first time and ending at a second time.The first time may be selected from the group consisting of no more thanabout 6 hours after sunset, no more than about 5 hours after sunset, nomore than about 4 hours after sunset, no more than about 3 hours aftersunset, and no more than about 2 hours after sunset. The second time maybe selected from the group consisting of the dew point convergence time,a time when the ambient temperature is at least about 0.5° F. above thedew point, a time when the ambient temperature is at least about 1.0° F.above the dew point, and a time when the ambient temperature is at leastabout 2.0° F. above the dew point.

It is known that factors such as humidity and distance from the surfacemay affect the output of the thermal image. Infrared cameras typicallyallow for inputs of the measured humidity and distance from surface.Humidity may be measured as either absolute humidity, relative humidity,or specific humidity using a psychrometer or hygrometer and input intothe infrared camera. Preferably, the humidity input to the infraredcamera is relative humidity. Distance from the surface may be measuredin standard or metric units using an altimeter and input into theinfrared camera.

The step of taking a visual image of the surface of the structure mayalso comprise flying over the surface using an airplane, helicopter,drone, or similar aerial vehicle. The aerial vehicle may include anoptical camera facing downwardly from the aerial vehicle towards theground. Preferably the optical camera is oriented towards the ground ina nadir position. One example of an optical camera is a Cannon 5D Seriescamera available from Canon Inc. of Ota, Tokyo, Japan. Preferably theoptical camera has a resolution of at least 10 megapixels with at least20 megapixels being more preferred.

The time of day that the visual image is taken is not consideredimportant. In fact, the visual image does not even have to be taken onthe same day as the thermal image. Preferably, the visual image is takenat or around solar noon on the same day as the thermal image. At oraround solar noon refers to the period beginning at least 15 minutesbefore solar noon and ending no more than 15 minutes after solar noon.By solar noon it is meant the time at which the sun crosses the localmeridian at the specific geographic location of the structure andreaches its highest position in the sky, except at the poles.

The step of conducting a thermal image numerical analysis on the thermalimage to obtain a thermal image numerical value table may compriseconverting the thermal image into a plurality of pixels with each pixelhaving an X coordinate and a Y coordinate. Each X coordinate and Ycoordinate may represent a specific position (i.e.—latitude andlongitude) on the surface. One example of a thermal image numericalvalue table is shown in FIG. 2 which is a thermal image numerical valuetable corresponding to the thermal image shown in FIG. 1 .

It is well known that the number of pixels and pixel size will depend,at least in part, on the resolution of the infrared camera used toobtain the thermal image and the distance from the surface from whichthe thermal image is taken. For instance, an infrared camera having aresolution of 320×240 taking a thermal image at a distance from thesurface of 50 feet may produce an image having 76,800 pixels with apixel size of 0.719×0.720 inches per pixel.

Each pixel may also have a Z coordinate. The Z coordinate may correspondto a thermal value of the pixel. It is well known that the thermal valueis a measurement of the infrared energy (heat) transmitted from thesurface and converted into an electronic signal by the infrared camera.The thermal value will be a real number greater than or equal to 0.

In some embodiments, the step of obtaining a surface nominal thermalvalue may comprise calculating the most prevalent thermal value(corresponding to the Z coordinate) across a subset of the pixels. Thesubset of pixels may comprise a percentage of the total pixels of thethermal image selected from the group consisting of at least 20% of thetotal pixels, at least 35% of the total pixels, at least 50% of thetotal pixels, at least 65% of the total pixels, at least 90% of thetotal pixels, at least 99% of the total pixels, and 100% of the totalpixels.

Once the surface nominal thermal value has been determined, a firstsubset of pixels from the plurality of pixels may be eliminated. Thisfirst subset of pixels may have a thermal value which is within anominal thermal variation of a surface material of the surface to thesurface nominal thermal value. For instance, for some surface materials,the nominal thermal variation of the surface material may be +/−1° F. ofthe surface nominal thermal value. The surface nominal thermal variationwill vary depending on the type of surface material. This first subsetof pixels are indicative of areas of the surface which do not requireremediation efforts as their thermal value does not significantlydeviate from the nominal thermal value. In some embodiments, the surfacenominal thermal variation may require a surface nominal thermalvariation adjustment based on environmental factors such as ambienttemperature or internal structure temperature. For instance, the surfacenominal thermal variation may be adjusted upwards by between 1% and 500%based on increases to the ambient temperature surrounding the exteriorof the surface or the temperature inside the structure which the surfacecovers.

Comparing one or more of the thermal values of the thermal imagenumerical value table to the surface nominal thermal value (NTV) toidentify at least one anomaly will yield an intensity value (IV) foreach thermal value (MTV) corresponding to each individual pixel uponwhich the comparison is run. The intensity value (IV) of each individualpixel is calculated according to Formula A:

(MTV)−(NTV)=IV   Formula A

It is preferred, but not necessary, that the intensity value iscalculated for each pixel of the plurality of pixels not in the firstsubset of pixels eliminated based on their deviation from the nominalthermal variation. In some embodiments, the intensity value may becalculated for a second subset of the plurality of pixels. The secondsubset of the plurality of pixel may comprise a percentage of the totalpixels of the thermal image selected from the group consisting of atleast 20% of the total pixels, at least 35% of the total pixels, atleast 50% of the total pixels, at least 65% of the total pixels, atleast 90% of the total pixels, at least 99% of the total pixels, and100% of the total pixels.

The intensity values may be plotted on a three dimensional graph ofintensity values. One example of such three dimensional graph is shownin FIG. 3 , which shows the three dimensional graph of intensity valuesfor the surface of a roof from FIG. 1 with the Z axis representing theintensity value which is a real number in the range of between 0 and 120calculated according to Formula A, and the X axis and Y axiscorresponding to the dimensions of the surface.

Once the intensity values are known they may be used to distinguish oneor more anomalies. Anomalies may be identified based on the minimum tomaximum rise above the nominal thermal value for the particular materialwhich makes up the surface. By way of example, but not limitation, wherethe surface is a roof and the material is a polyisocyanurate, theminimum rise above nominal thermal value is +2.5° F. while the maximumrise above nominal thermal value is +30° F. Accordingly, if the nominalthermal value of a polyisocyanurate roof is determined to be 77° F., aparticular pixel would qualify as an anomaly if its thermal value isless than 79.5° F. or greater than 107° F.

In some embodiments, the maximum rise above nominal thermal value forthe particular material may require a first adjustment due to theeffects of increased daytime high temperature on the material. Saidfirst adjustment may include the addition of 0.65° F. to the maximumrise above nominal thermal value for every degree Fahrenheit that thedaytime high temperature is above 78° F. to obtain a first adjustedmaximum rise above nominal thermal value. By way of example, but notlimitation, where the surface is a polyisocyanurate roof, and themeasured local daytime high temperature is 105° F., the maximum riseabove nominal thermal value should be increased by 17.55° F., making thefirst adjusted maximum rise above nominal thermal value for thepolyisocyanurate roof +47.55° F. Accordingly, in the example listedabove where the surface is a polyisocyanurate roof and the nominalthermal value is determined to be 77° F., a particular pixel wouldqualify as an anomaly if its thermal value is less than 79.5° F. orgreater than 124.55° F.

In some embodiments, the maximum rise above nominal thermal value forthe particular material may require a second adjustment due to theeffects of dew point to obtain a second adjusted maximum rise abovenominal thermal value. Said second adjustment may include determining anestimated time remaining in the thermal image time window at the actualtime of taking the thermal image. For instance, if the thermal imagetime window is set to open at 8:00 pm and close at 11:15 pm (based onforecasted temperature and dew point used to calculate the second timeat which the thermal image time window closes according to the methoddescribed herein), then the thermal image time window is scheduled tolast 195 minutes. If the thermal image actual time (the time at whichthe thermal image was actually taken) is 9:30 pm, then the thermal imagewas taken with 54.8% of the thermal image time window remaining. Thefirst adjusted maximum rise above nominal thermal value is thenmultiplied by the percent of thermal image time window remaining todetermine the second adjusted maximum rise above nominal thermal value.In the example above where the surface is a polyisocyanurate roof andthe first adjusted maximum rise above nominal thermal value wasdetermined to be +47.55° F., if the percentage of thermal image timewindow remaining is 54.8% as calculated above, then the second adjustedmaximum rise above nominal thermal value is +26.1° F. Accordingly, inthe example listed above where the surface is a polyisocyanurate roofand the nominal thermal value is determined to be 77° F., a particularpixel would qualify as an anomaly if its thermal value is less than79.5° F. or greater than 103.1° F.

In some embodiments, the effect of wind velocity at the actual time thatthe thermal image is taken may result in an adjustment to the thermalimage time window for purposes of calculating the second adjustedmaximum rise above nominal thermal value. In the event that windvelocity at the actual time that the thermal image is taken is in arange of 5 knots to 9 knots, then the first time of the thermal imagetime window may be adjusted by −10 minutes (i.e.—the thermal image timewindow opens 10 minutes earlier) while the second time of the thermalimage time window may be adjusted by −15 minutes (i.e.—the thermal imagetime window closes 15 minutes earlier) for purposes of calculating thesecond adjusted maximum rise above nominal thermal value. In the eventthat wind velocity at the actual time that the thermal image is taken isin a range of 10 knots to 15 knots, then the first time of the thermalimage time window may be adjusted by −20 minutes (i.e.—the thermal imagetime window opens 20 minutes earlier) while the second time of thethermal image time window may be adjusted by −30 minutes (i.e.—thethermal image time window closes 30 minutes earlier) for purposes ofcalculating the second adjusted maximum rise above nominal thermalvalue.

The intensity values may be used to remove at least a first number (n₁)of first anomalies from the thermal image numerical analysis. The firstanomalies may be selected from the group of anomalies consisting of anon-delta anomaly, a non-signature anomaly, a non-pattern anomaly, anon-visual anomaly, and a non-cavity anomaly.

A non-delta anomaly is one type of anomaly having a slope which is notindicative of a moisture anomaly. Slope may be measured as the delta ofthermal or intensity value between two or more adjacent pixels.Non-delta anomalies may be indicated in the thermal image numericalvalue table by a pixel or cluster of pixels whose intensity valuedeviates from the intensity value of one or more adjacent pixels bybetween −5 and +5 intensity value units.

A non-signature anomaly is another type of anomaly having a slope whichis not indicative of a moisture anomaly. Signature anomalies may beindicated by an intensity value slope which is not in the range ofbetween 0.15 and 0.75 intensity value units. The slope is measured bycomparing the intensity value of at least one pixel to at least oneadjacent pixel(s) along either the X axis or the Y axis.

A non-pattern anomaly is another type of anomaly having a slope which isnot indicative of a moisture anomaly. Non-pattern anomalies areindicative of an area which does not comprise the surface material, orcomprises a material additional to the surface material. Such areas maycomprise a coating or paint either on or below the surface material,and/or they may comprise a non-surface material laid on top of orunderneath the surface material. Non-pattern anomalies may be indicatedby a slope having a defined edge along the X axis and/or the Y axisbetween the intensity value of a first cluster of pixels and at least asecond cluster of pixels. While moisture anomalies will typically havean amorphous shape without defined edges, a non-pattern anomaly willhave a defined shape with defined edges.

A non-visual anomaly is another type of anomaly having a slope which isnot indicative of a moisture anomaly. Non-visual anomalies areindicative of an object installed on the surface. For example, when thesurface is a roof, the non-visual anomaly may be a rooftop mounted ventor HVAC unit. Visual anomalies may be detected by comparing the thermalimage numerical value table to the visual image.

A non-cavity anomaly is another type of anomaly having a slope which isnot indicative of a moisture anomaly. Non-cavity anomalies are areas inwhich the temperature influence of an object adjacent to a portion ofthe surface causes interference with the measured thermal value of acluster of pixels corresponding to that portion of the surface. Forexample, when the surface is a roof of a building, and the buildingincludes walls—a portion of which extend upward relative to gravitybeyond the roof surface—heat may reflect off of the wall onto a portionof the roof surface adjacent to the wall causing an increased thermalvalue reading for the roof surface which is adjacent to the wall. Otherobjects may include a vent, an HVAC unit, or other physicalconfiguration of the surface which extend upwardly from the surface ordownwardly into the surface. Non-cavity anomalies may be indicated by anelevated thermal value of a pixel which is not amorphous and is locatedproximate to a physical configuration on or in the surface as shown inthe infrared image and/or the visual image.

The first number (n₁) of first anomalies may be removed in any sequence.The sequence of removing the first number (n₁) of first anomalies is notbelieved important. In some embodiments, multiple types of firstanomalies may be removed simultaneously. In other embodiments, all ofthe types of first anomalies may be removed simultaneously.

After removing the first number (n₁) of first anomalies, one may comparethe first anomalies from the thermal image numerical analysis to thevisual image. This comparison allows for visual confirmation of thefirst anomalies.

In some embodiments, after removing the first number (n₁) of firstanomalies, one may identify a second number (n₂) of second anomaliesfrom the thermal image numerical analysis. The second anomalies may beindicative of a moisture anomaly. Detection of a moisture anomaly maycall for conducting any number of repair efforts on the specifictargeted area(s) of the surface where the moisture anomaly has beendetected. Such moisture anomalies may then be categorized as either anenergy loss anomaly or a live load anomaly.

An energy loss anomaly is indicative of a reduction in the surfacematerial's ability to resist heat flow—also known as thermal resistance.Thermal resistance of materials is commonly expressed as an R value, andmay be calculated by a number of methods. One common method forcalculating an R value of particular materials are typically publishedby the particular material manufacturer. Energy loss anomalies may beidentified by first comparing a maximum thermal value loss indicative of100% moisture content for the surface material to an observed thermalvalue loss of the surface material across each individual pixel of theplurality of pixels to obtain a moisture content level. The moisturecontent level may be calculated according to the following formula:

$\frac{\Delta T_{Obs}}{\Delta T_{Max}} = {MC}$

where ΔT_(obs) is the measured thermal value loss of the surfacematerial, ΔT_(Max) is the maximum thermal value loss indicative of 100%moisture content for the surface material, and MC is the calculatedmoisture content level. The calculated moisture content level may thenbe compared to a known R value loss chart for the particular surfacematerial to arrive at a percent R value reduction. Known R value losscharts exist for many different types of materials in many differentindustries. The percent R value reduction may be calculated according tothe following formula:

${\frac{MC}{RL} = \%}{Reduction}$

where MC is the calculated moisture content level, and RL is the known Rvalue loss of the particular surface material. The percent R valuereduction may be calculated for an individual pixel, across the entiresurface, or across one or more subsets of the surface. When the percentR value reduction is calculated across the entire surface, the ΔT_(Obs)will be the average of ΔT_(Obs) for each pixel of the plurality ofpixels which make up the surface. When the percent R value reduction iscalculated across one or more subsets of the surface, the ΔT_(Obs) willbe the average of ΔT_(Obs) for each pixel which makes up the subset(s)of the surface. Detection of an energy loss anomaly may call forconducting any number of energy loss mitigation efforts on the specifictargeted area(s) of the surface where the energy loss anomaly has beendetected. Typically a percent R value reduction of at least 5% isindicative of an energy loss anomaly which requires mitigation efforts.

A live load anomaly is indicative of surface damage due to weight addedto the surface after installation. Examples might include a personwalking on the surface or snow accumulation sitting on the surface. Liveload anomalies may be calculated by determining the square footage of ananomaly and multiplying the square footage by the thermal value deltafrom the nominal thermal value of each of the pixels corresponding tothe anomaly. Detection of a live load anomaly may call for conductingany number of repair efforts on the specific targeted area(s) of thesurface where the live load anomaly has been detected.

The method disclosed herein is an improvement over known methods fordetecting and monitoring anomalies on the surface of a structure. Byeliminating anomalies which are not of interest—such as delta anomalies,signature anomalies, pattern anomalies, visual anomalies, and cavityanomalies—repair and mitigation efforts can be focused on only thoseareas where anomalies of interest are located. For instance, instead ofreplacing an entire roof, the structure owner may be able to replace orrepair only a certain section of a roof where an anomaly of interest,such as a moisture anomaly, has been detected. This may save thestructure owner considerable time and monetary resources.

The method disclosed herein may be conducted on any number of types ofsurfaces. For instance, the surface may be a roof or wall of a building.In another example, the surface may be the top of a landfill. In yetanother example the surface may be the deck of a bridge. In yet anotherexample the surface may be a road, parking lot, or other paved surface.In still another example the surface may be an agricultural field.

Another type of surface anomaly may be a lighting anomaly. Lightinganomalies may include areas of non-existent or insufficient illuminationat or near the surface during non-daylight hours. For many surfaces—suchas the surface of a parking lot—these areas of non-existent orinsufficient illumination can lead to dangerous events such aspedestrians slipping or falling, and criminal activities.

The method of detecting lighting anomalies on a surface of a structuremay comprise a number of steps. A first step in such method may includetaking a visual image of the surface of the structure. The visual imageof the surface of the structure may be taken in color, greyscale, orblack and white—but is preferably taken in color. The visual image ispreferably taken by an optical camera mounted to an aircraft such as anairplane, helicopter, or drone flying over the surface. The opticalcamera preferably has a resolution of at least 4 megapixels with atleast 15 megapixels being more preferred, and preferably has a shutterspeed set in the range of between 1/400 seconds and 1/10000 second withbetween 1/450 and 1/9000 being more preferred and between 1/500 secondsand 1/8000 seconds being most preferred. When the aircraft is ahelicopter or drone, the aircraft may be traveling at a speed in therange of between 0 mph (hovering still) and 10 mph with a speed in therange of between 1 mph and 7.5 mph being more preferred and between 2mph and 5 mph being most preferred. When the aircraft is an airplane,the aircraft may be traveling at a speed in the range of between 75 mphand 150 mph with a speed in the range of 85 mph and 140 mph being morepreferred and a speed in the range of between 100 mph and 125 mph beingmost preferred. Preferably the aircraft will be at an altitude ofbetween 300 feet from the surface and 2,000 feet from the surface withbetween 350 feet from the surface and 1,750 feet from the surface beingmore preferred and between 400 feet from the surface and 1,500 feet fromthe surface being most preferred.

The visual image is preferably taken during a time of darkness. The timeof darkness preferably occurs during the period no greater than 15minutes after sunset and no less than 15 minutes before sunrise, withthe period no greater than 30 minutes after sunset and no less than 30minutes before sunrise being more preferred, the period no greater than45 minutes after sunset and no less than 45 minutes before sunrise beingstill more preferred, and the period no greater than 60 minutes aftersunset and no less than 60 minutes before sunrise being most preferred.

In some embodiments, the visual image may be taken at or around the sametime as the thermal image. This thermal image may then be used as acomparison to determine if existing lighting elements at or near thesurface are functioning properly by referring to the amount of heatemitted by lighting elements in known areas at or near the surface. Aknown lighting element which is not emitting heat as shown in thethermal image, or which is emitting less heat than other known lightingelements of the same or similar type, would indicate a lighting elementwhich requires repair or replacement.

After taking the visual image of the surface of the structure, a blackand white color adjustment may be conducted on the visual image. Blackand white color adjustments are common and well known on many digitalcameras and photo editing software programs used in a variety ofindustries. The black and white color adjustment changes each pixelwhich makes up the visual image of the surface from its specific colorto black, white, or a shade of gray depending upon a number of factorsincluding the specific color of the individual picture.

After performing the black and white color adjustment a light intensityof a plurality of pixels within the visual image is calculated inhorizontal foot candles and/or vertical foot candles using knownsoftware applications such as PhotoShop® or ImageJ which utilize thehistogram feature to determine the mean light intensity value of theplurality of pixels within the visual image. Preferably the plurality ofpixels upon which the light intensity calculation is conducted comprises100% of the pixels within the visual image, however, in some embodimentsthe plurality of pixels may include up to 90% of the pixels within thevisual image, up to 75% of the pixels within the visual image, up to 50%of the pixels within the visual image, up to 25% of the pixels withinthe visual image, or up to 10% of the pixels within the visual image.

Once the light intensity of the plurality of pixels within the visualimage has been calculated, a first subset of the plurality of pixels maybe identified. This first subset of the plurality of pixels correspondsto a threshold light intensity. Preferably, the threshold lightintensity when measured in horizontal foot candles is in a range ofbetween 0.75 fc and 3 fc with a range of between 1 fc and 2.75 fc beingmore preferred and a range of between 1.25 fc and 2.5 fc being mostpreferred. When measured in vertical foot candles, the threshold lightintensity is preferably between 0.4 fc and 1.6 fc with a range ofbetween 0.5 fc and 1.5 fc being more preferred and a range of between0.6 fc and 1.4 fc being most preferred.

Once the threshold light intensity level has been determined, a secondsubset of the plurality of pixels may be identified. Each pixel withinthe second subset of the plurality of pixels will have a light intensitywhich is below the threshold light intensity. This second subset of theplurality of pixels may then be subjected to a black color adjustment. Ablack color adjustment indicates that the color of each pixel within thesecond subset of pixels is changed to black from its previously existingcolor. The black color adjustment sets each pixel within the secondsubset of the plurality of pixels to a first color value that is no morethan 15% above black (Color code RGBA=#0.0,0.0,0.0; Hue:0, Saturation:0,and Value:0.0) with no more than 10% above black being more preferredand no more than 5% above black being most preferred.

Next, a third subset of the plurality of pixels may be identified. Eachpixel within the third subset of the plurality of pixels will have alight intensity which is above the threshold light intensity. This thirdsubset of the plurality of pixels may then be subjected to a gradationcolor adjustment. A gradation color adjustment indicates that the colorof each pixel within the third subset of pixels is changed from itspreviously existing color with pixels having a higher light intensitybeing changed to white or lighter shades of gray and pixels having lowerlight intensity (but still above the threshold light intensity) beingchanged to darker shades of gray. Preferably the pixels corresponding tothe highest light intensity value are set to a second color value thatis no more than 15% below white (Color code RGBA=#255,255,255; Hue:0,Saturation:0, and Value:100) with no more than 10% below white beingmore preferred and no more than 5% below white being most preferred.Pixels between the threshold light intensity value and the highest lightintensity value are set to a color value that is between the first colorvalue of the second subset of the plurality of pixels and the secondcolor value of the pixels corresponding to the highest light intensityvalue. The color value for such pixels will vary with pixels havinghigher light intensity values being set to a color value which is closerto the second color value and pixels having lower light intensity valuesbeing set to a color value which is closer to the first color value.

The final image after the black color adjustment and gradation coloradjustment may be utilized to identify areas of non-existent orinsufficient illumination which may call for remediation efforts such asthe addition of new lighting elements or the adjustment of existinglighting elements. For instance, areas of the surface corresponding tothe second subset of the plurality of pixels which have been subjectedto a black color adjustment would call for remediation efforts. In someembodiments, areas of the surface corresponding to the third subset ofthe plurality of pixels which have been adjusted to have darker shadesof gray by the gradation color adjustment may also call for remediationefforts.

The final image may be validated by way of a digital light meteroperated at surface level to take spot readings from within theboundaries of the visual image. One preferred digital light meter is anIDEAL Electrical 61-686 Digital Light Meter available from IdealIndustries (Canada) Corp. of Ontario, Canada.

One or more of the method steps disclosed herein may be conducted via acomputer. Method steps which may be conducted via a computer include—butare not limited to—obtaining a thermal image numerical value table,obtaining a surface nominal thermal value, eliminating a first subset ofpixels from the plurality of pixels, comparing a thermal value of eachpixel of the plurality of pixels not in the first subset of pixels tothe surface nominal thermal value, removing a first number of firstanomalies from the thermal image numerical value table, comparing thethermal value of a second number of second anomalies from the thermalimage numerical value table, comparing the first anomalies from thethermal image numerical analysis to the visual image, calculating theintensity value, performing a black and white color adjustment,calculating a lighting intensity, identifying a first subset of theplurality of pixels, identifying a second subset of the plurality ofpixels, performing a black color adjustment, identifying a third subsetof the plurality of pixels, and performing a gradation color adjustment.

1. A method of detecting anomalies on a surface comprising: taking a thermal image of the surface, conducting a thermal image numerical analysis on the thermal image wherein the thermal image numerical analysis comprises the steps of: i. obtaining a thermal image numerical value table comprising a plurality of pixels with each pixel having an X coordinate, a Y coordinate, and a Z coordinate with the X coordinate and the Y coordinate corresponding to a specific spatial location of the pixel on the surface, and the Z coordinate corresponding to a thermal value of said pixel which is a real number greater than or equal to 0; ii. obtaining a surface nominal thermal value; iii. eliminating a first subset of pixels from the plurality of pixels wherein each of the first subset of pixels has a thermal value which is within a nominal thermal variation of a surface material of the surface to the surface nominal thermal value; iv. comparing a thermal value of each pixel of the plurality of pixels not in the first subset of pixels to the surface nominal thermal value to identify at least one anomaly; v. comparing the thermal value of a second number (n2) of a second anomalies from the thermal image numerical value table wherein n2 is an integer greater than or equal to 0 to the surface nominal thermal value in order to identify a type of anomaly selected from the group consisting of an energy loss anomaly, and a live load anomaly, and vi. removing a first number (n₁) of first anomalies from the thermal image numerical value table wherein n₁ is an integer greater than or equal to 0, and the first anomalies are selected from the group consisting of at least one non-delta anomaly, at least one non-signature anomaly, at least one non-pattern anomaly, at least one non-visual anomaly, and at least one non-cavity anomaly wherein the surface is selected from the group consisting of a road, a parking lot, a paved surface, and a deck of a bridge.
 2. The method of claim 1, wherein the thermal image is taken at least 60 minutes after sunset on a sunny day.
 3. The method of claim 2, wherein the thermal image is taken at a time selected from the group consisting of no more than about 6 hours after sunset, no more than about 5 hours after sunset, no more than about 4 hours sunset, no more than about 3 hours after sunset, and no more than about 2 hours after sunset.
 4. The method of claim 2, wherein the thermal image is taken on a day in which a geographic location of the surface experiences no more than about 40% cloud cover during at least the 4 hour preceding sunset.
 5. The method of claim 2, wherein the thermal image is taken on a day in which a geographic location of the surface experiences no more than about 30% cloud cover during at least the 4 hour preceding sunset.
 6. The method of claim 2, wherein the thermal image is taken on a day in which a geographic location of the surface experiences no more than about 20% cloud cover during at least the 4 hour preceding sunset.
 7. The method of claim 2, wherein the thermal image is taken on a day in which a geographic location of the surface experiences no more than about 10% cloud cover during at least the 4 hour preceding sunset.
 8. The method of claim 1, further comprising the steps of: C. taking a visual image of the surface; and D. comparing the first anomalies from the thermal image numerical analysis to the visual image.
 9. The method of claim 8, wherein the visual image is taken at or around solar noon on the same day as the thermal image.
 10. The method of claim 1, wherein obtaining the surface nominal thermal value comprises calculating the most prevalent thermal value across a second subset of the pixels.
 11. The method of claim 10, wherein the second subset of the plurality of pixels comprises a percentage of total pixels of the thermal image selected from the group consisting of at least 20% of the total pixels, at least 35% of the total pixels, at least 50% of the total pixels, at least 65% of the total pixels, at least 90% of the total pixels, at least 99% of the total pixels, and 100% of the total pixels.
 12. The method of claim 1, wherein the step of comparing a thermal value of each pixel of the plurality of pixels to the surface nominal thermal value yields an intensity value for each thermal value corresponding to each individual pixel calculated according to a formula: (MTV)−(NTV)=IV where MTV is the thermal value of each pixel and NTV is the surface nominal thermal value.
 13. The method of claim 12, wherein the intensity value is calculated for each pixel of the plurality of pixels which make up the thermal image numerical value table.
 14. The method of claim 12, wherein the intensity value is calculated for a third subset of the plurality of pixels comprising a percentage of the total pixels of the thermal image selected from the group consisting of at least 20% of the total pixels, at least 35% of the total pixels, at least 50% of the total pixels, at least 65% of the total pixels, at least 90% of the total pixels, at least 99% of the total pixels, and 100% of the total pixels.
 15. The method of claim 12, wherein the intensity value of each pixel of the plurality of pixels is plotted on a three dimensional graph.
 16. The method of claim 1, wherein obtaining the surface nominal thermal value comprises calculating the most prevalent thermal value across a second subset of the pixels.
 17. The method of claim 16, wherein the second subset of the plurality of pixels comprises a percentage of total pixels of the thermal image selected from the group consisting of at least 20% of the total pixels, at least 35% of the total pixels, at least 50% of the total pixels, at least 65% of the total pixels, at least 90% of the total pixels, at least 99% of the total pixels, and 100% of the total pixels.
 18. The method of claim 1, wherein the step of comparing a thermal value of each pixel of the plurality of pixels to the surface nominal thermal value yields an intensity value for each thermal value corresponding to each individual pixel calculated according to a formula: (MTV)−(NTV)=IV where MTV is the thermal value of each pixel and NTV is the surface nominal thermal value.
 19. The method of claim 18, wherein the intensity value is calculated for each pixel of the plurality of pixels which make up the thermal image numerical value table. 